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The Amazing World ofPoker and Artificial Intelligence

Poker is one of the world’s most popular card games. It offers high stakes, dynamic action and a chance for players to bluff their opponents into some seriously big wins. Although skill, proper strategy, and luck are all important aspects of poker, perhaps the most important fact is that it is a social game, and one that requires a fair share of mastering psychology.

What if the player sitting opposite from you was not a person, but instead it was a computer? Sure, you can head over to your favorite online casino or land-based venue to play video poker, and there is nothing wrong with that, in fact you might just win big.

However, video poker is a different type of game, which relies on random number generators against the player. We are talking about live poker, a game where the elements can change with each flip of a card; forcing players to think fast and use the best of their abilities to come out on top.

It takes years to master the kind of skill required to play poker at the highest levels, unless of course you possess a super brain. As it turns out, there is just such a brain, but it’s an artificially intelligent supercomputer constructed by lab geeks with the power to beat some of the world’s best poker players. It is time for us to introduce you to Libratus – the poker playing A.I. robot.

High stakes

The setting was the Rivers Casino in Pittsburgh, the occasion a 20-day poker marathon dubbed “Brains vs. Intelligence: Upping the Ante”, and the competitors were four world poker pros up against Libratus, an artificial intelligence supercomputer developed by the prestigious Carnegie Mellon University.

On the last leg of the tournament, Libratus made history as it defeated its human counterparts out of a total 120,000 hands in Heads-up, No-Limit Texas Hold’em poker. Remarkably, the professional poker talent were collectively down $1,766,250 in gambling chips, and they were out of options.CoolCat Bonus Codes Tuomas Sandholm, professor of computer sciences at CMU, and Noam Brown, a Ph.D. student in computer science smirked, as they knew that their machine had crushed its opponents in poker, not by using luck, but mathematics, probability and statistics.

Much like when world champion Garry Kasparov lost to the IBM supercomputer Deep Blue in a series of chess games some twenty years prior, Libratus was the next generation of A.I. capable of defeating a herd of opponents at one of the world’s most beloved card games. A machine with the ability to use strategic reasoning with imperfect information to surpass the best human players.

Big bluffing

This incredible milestone in artificial intelligence has serious implications for the realm of not only poker, but also information technology, business negotiation, military strategy, cybersecurity and medical treatment planning; the critical factor being the computer’s superior ability to solve problems and exhibit critical decision making.

The reason Libratus’ victory over its fleshy foes was more remarkable than that of Deep Blue is due to the game dynamics of poker. Chess relies on thousands upon thousands of permutations of play options each time an opponent makes a move. A computer can study all of the possible combinations and react accordingly, feigning creativity and intelligence in the process. In poker however, bluffing, or deceiving one’s opponent is a crucial aspect of the game, creating immense unpredictability in determining a winner regardless of who has the better hand.

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“The computer can’t win at poker if it can’t bluff,” said Frank Pfenning, head of the Computer Sciences Department at CMU’s School of Computer Science. “Developing an A.I. that can do that successfully is a tremendous step forward scientifically and has numerous applications. Imagine that your smartphone will someday be able to negotiate the best price on a new car for you.”

Players in the game

Against Liberatus was a poker pro team consisting of Dong Kim, Jimmy Chou, Daniel McAulay and Jason Les, and although losing, the team managed to split a $200,000 prize purse based on their respective performances in the event. “Tougher than expected” is how one player described playing Libratus, “but exciting to play against”. As the pros put it, whenever you play against top players, you improve, and this team actually learned from Libratus, instead of just the other way around.

The objective of the experiment was to gather the best professional Heads-up No-Limit Texas Hold’em poker players in the world who would be willing to go up against a machine at their own game. The team would have to work as a group to beat Libratus over the course of 20 grueling days, playing hours and hours of poker. The Pittsburgh Supercomputing Center’s Bridges computer were the mechanical components for Libratus. However, it was CMU’s Computer Sciences team, which not only developed the software, but also implemented its stellar poker strategy.

A.I. poker strategy

Early on in the poker event, it became apparent that Libratus did not just have a solid poker game plan, but was able to improve its performance as the days of the competition progressed. At first, the pros were able to utilize the weaknesses in Libratus’ game. However, each new day revealed that they were facing a smarter, more intelligent opponent, which eventually led to their downfall. Not since Stu Ungar and the 1980 World Series of Poker, had a poker tournament seen a player improve so significantly in just a matter of days. Like a human, the A.I. was learning in real time.

According to Sandholm, Libratus utilized a meta-algorithm, which at the end of each day of play would analyze holes in its own game, evolving its poker strategy accordingly. Previous models exploited opponent’s weaknesses, but in contrast, the daily improvement of Libratus fixed holes in its poker strategy, much like a human player would.

Libratus improved its play methods so successfully that it was able to ensure that any late changes in each match would only further benefit its progression. With each new hand, the pros responded aggressively to avoid Libratus’ play advantage in the deep waters of the endgame, but as the days went on their tactics ultimately proved unsuccessful. They were simply no match for Libratus’ bone chilling poker strategy.

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Poker mechanics and a super brain

Head’s-Up No-Limit Texas Hold’em is an extraordinarily complex game. To prove just how remarkable Libratus’ feat in calculating effective poker odds and strategizing accordingly was, in terms of numbers, Hold’em holds 1^160 (one followed by 160 zeros) possible information sets, each characterized by the path of play in the hand perceived by the player whose turn it is.

Put into practical terms, the number of potential information sets and sequences in poker is greater than the approximate number of atoms in the universe!

In real time, Libratus made decisions without full knowledge of all of the cards in play, while simultaneously sniffing out potential bluffs from opponents purely through use of mathematical probabilities; all made possible with the raw power of approximately 846 Bridges’ computer nodes. With a total speed of 1.35 petaflops (that’s 1,000 teraflops), roughly 7,250 times faster than today’s high-end commercial laptops, and with a memory capable of storing 274 Terabytes of information, it seems that not only was Libratus able to play four of the best Texas Hold’em players in the world, but broke their bank easily.

Pfenning stated, “It has been very exciting to watch the progress of poker-playing programs that have finally surpassed the best human players. Each one of these accomplishments represents a major milestone in our understanding of intelligence.”

Sandholm added that use the information gained from Libratus to push his research further, expanding it to crack other imperfect-information games, as well as aid in solving real-world problems. It seems that poker is doing good for the world, and not just as a form of entertainment.

Conclusion

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Eliot Applegate

Eliot is a University of Kansas alumni, which is where he discovered his love of competitive games. The ultimate triathlon junkie, when he isn't on the track or in the pool he can be found playing poker and searching for the next major European casino tournament.